1. Field of the Invention
The present invention relates particularly to an animation image generating program suited for face animations depicting mouth movements which correspond to speeches.
2. Description of the Related Art
Recent years have seen creation of three-dimensional (3D) animation images and display of their pictures using computer graphics (CG) techniques.
In such a field, moving pictures are displayed which depict mouth movements and facial expressions corresponding to characters' moment-by-moment speeches and emotions as a form of animation. Until today, such moving pictures have been created by creating an animation pattern image of facial model for each frame of characters' movements, that is, by moving the vertices of the facial model or moving the “bones” incorporated into the model.
Thus, an animation pattern image of the facial model has been created for each picture frame to create face animations depicting speeches and facial expressions in CG. These images have been arranged along the time-axis and reproduced continuously.
Therefore, images of all facial expression shapes and all mouth shapes during speeches are created prior to animation generating tasks to create face animations of a certain character model. The images are further combined to create the whole.
Moreover, shape data of a certain model A cannot be used for the facial shape of other character model B. Therefore, similar image data must be created for all characters, resulting in enormous costs and amounts of time needed for creation.
A technique using an “artificial joint mechanism” called the skeleton has been traditionally employed for deforming certain three-dimensional computer graphics (3DCG). However, creation of speeches or expressions with the skeleton is difficult since face does not move with joints as arms and legs do.
The prior art called the Wrap Deformation allows changes made to a certain model shape A to be reflected in other model shape B. An example of calculations by the Wrap Deformation is as shown below.
Assuming that both the models A and B have six vertices each,                Model A's vertex 1 (X coordinate)=w1x model B's vertex 1 (X coordinate)+w2x model B's vertex 2 (X coordinate)+w3x model B's vertex 3 (X coordinate)+w4x model B's vertex 4 (X coordinate)+w5X model B's vertex 5 (X coordinate)+w6x model B's vertex 6 (X coordinate)        Model A's vertex 1 (Y coordinate)=Same as above        Model A's vertex 1 (Z coordinate)=Same as above        
where wn (n=1 to 6) is a weight coefficient assigned from each of the vertice sof the model B to a specific vertex of the model A.
As is apparent from the above calculation example, there are not many calculations involved when models have only a small number of vertices (10 or so), thus allowing the PC to perform calculation operations without laboring. However, an enormous amount of calculations is required if the number of vertices amounts to 1,000 to over 100,000 as in the case of facial models used in ordinary 3DCG, thus making it almost impossible to obtain realistic results with PCs available today.